Development of a scale to measure the psychosocial impact of assistive devices: lessons learned and the road ahead
Bibliographic record
Abstract
PURPOSE: In this paper the history of the development and validation of the PIADS is reviewed. Assistive devices (ADs) are extremely prevalent forms of health care intervention for persons who have a disability. There is a consensus that the AD field needs a reliable and valid measure of how users perceive the impact of ADs on their quality of life (QoL) and sense of well-being. The Psychosocial Impact of Assistive Devices Scale (PIADS) is a 26 item self-rating scale designed to fill this measurement gap. The challenges that we encountered are described in attempting to adequately conceptualize QOL impact, and operationalize it in a measure suitable for use with virtually all forms of AD. Current efforts to extend the validation of the PIADS are summarized. CONCLUSIONS: The study concludes by suggesting directions for future research and development of the scale. They include a richer examination of its conceptual relationships to other health care and rehabilitation outcome measures, and further investigation of its clinical utility. The PIADS is a reliable and valid tool that appears to have very significant power to predict AD abandonment and retention. It can and should be used both deductively and inductively to build, discover and test theory about the psychosocial impact of assistive technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".